IMPORTANCE Health care workers exposed to coronavirus disease 2019 (COVID-19) could be psychologically stressed. OBJECTIVE To assess the magnitude of mental health outcomes and associated factors among health care workers treating patients exposed to COVID-19 in China. DESIGN, SETTINGS, AND PARTICIPANTS This cross-sectional, survey-based, region-stratified study collected demographic data and mental health measurements from 1257 health care workers in 34 hospitals from January 29, 2020, to February 3, 2020, in China. Health care workers in hospitals equipped with fever clinics or wards for patients with COVID-19 were eligible. MAIN OUTCOMES AND MEASURESThe degree of symptoms of depression, anxiety, insomnia, and distress was assessed by the Chinese versions of the 9-item Patient Health Questionnaire, the 7-item Generalized Anxiety Disorder scale, the 7-item Insomnia Severity Index, and the 22-item Impact of Event Scale-Revised, respectively. Multivariable logistic regression analysis was performed to identify factors associated with mental health outcomes. RESULTSA total of 1257 of 1830 contacted individuals completed the survey, with a participation rate of 68.7%. A total of 813 (64.7%) were aged 26 to 40 years, and 964 (76.7%) were women. Of all participants, 764 (60.8%) were nurses, and 493 (39.2%) were physicians; 760 (60.5%) worked in hospitals in Wuhan, and 522 (41.5%) were frontline health care workers. A considerable proportion of participants reported symptoms of depression (634 [50.4%]), anxiety (560 [44.6%]), insomnia (427 [34.0%]), and distress (899 [71.5%]). Nurses, women, frontline health care workers, and those working in Wuhan, China, reported more severe degrees of all measurements of mental health symptoms than other health care workers (eg, median [IQR] Patient Health Questionnaire scores among physicians vs nurses: 4.0 [1.0-7.0] vs 5.0 [2.0-8.0]; P = .007; median [interquartile range {IQR}] Generalized Anxiety Disorder scale scores among men vs women

psychological protection of the mental health of medical workers has been initiated in China. The experiences from this public health emergency should inform the efficiency and quality of future crisis intervention of the Chinese Government and authorities around the world.

This study aims to characterize the gut microbiota in depressed patients with bipolar disorder (BD) compared with healthy controls (HCs), to examine the effects of quetiapine treatment on the microbiota, and to explore the potential of microbiota as a biomarker for BD diagnosis and treatment outcome. Analysis of 16S‐ribosomal RNA gene sequences reveals that gut microbial composition and diversity are significantly different between BD patients and HCs. Phylum Bacteroidetes and Firmicutes are the predominant bacterial communities in BD patients and HCs, respectively. Lower levels of butyrate‐producing bacteria are observed in untreated patients. Microbial composition changes following quetiapine treatment in BD patients. Notably, 30 microbial markers are identified on a random forest model and achieve an area under the curve (AUC) of 0.81 between untreated patients and HCs. Ten microbial markers are identified with the AUC of 0.93 between responder and nonresponder patients. This study characterizes the gut microbiota in BD and is the first to evaluate microbial changes following quetiapine monotherapy. Gut microbiota‐based biomarkers may be helpful in BD diagnosis and predicting treatment outcome, which need further validations.

Gut microbiome disturbances have been implicated in major depressive disorder (MDD). However, little is known about how the gut virome, microbiome, and fecal metabolome change, and how they interact in MDD. Here, using whole-genome shotgun metagenomic and untargeted metabolomic methods, we identified 3 bacteriophages, 47 bacterial species, and 50 fecal metabolites showing notable differences in abundance between MDD patients and healthy controls (HCs). Patients with MDD were mainly characterized by increased abundance of the genus Bacteroides and decreased abundance of the genera Blautia and Eubacterium. These multilevel omics alterations generated a characteristic MDD coexpression network. Disturbed microbial genes and fecal metabolites were consistently mapped to amino acid (γ-aminobutyrate, phenylalanine, and tryptophan) metabolism. Furthermore, we identified a combinatorial marker panel that robustly discriminated MDD from HC individuals in both the discovery and validation sets. Our findings provide a deep insight into understanding of the roles of disturbed gut ecosystem in MDD.

Discriminating depressive episodes of bipolar disorder (BD) from major depressive disorder (MDD) is a major clinical challenge. Recently, gut microbiome alterations are implicated in these two mood disorders; however, little is known about the shared and distinct microbial characteristics in MDD versus BD. Here, using 16S ribosomal RNA (rRNA) gene sequencing, the microbial compositions of 165 subjects with MDD are compared with 217 BD, and 217 healthy controls (HCs). It is found that the microbial compositions are different between the three groups. Compared to HCs, MDD is characterized by altered covarying operational taxonomic units (OTUs) assigned to the Bacteroidaceae family, and BD shows disturbed covarying OTUs belonging to Lachnospiraceae, Prevotellaceae, and Ruminococcaceae families. Furthermore, a signature of 26 OTUs is identified that can distinguish patients with MDD from those with BD or HCs, with area under the curve (AUC) values ranging from 0.961 to 0.986 in discovery sets, and 0.702 to 0.741 in validation sets. Moreover, 4 of 26 microbial markers correlate with disease severity in MDD or BD. Together, distinct gut microbial compositions are identified in MDD compared to BD and HCs, and a novel marker panel is provided for distinguishing MDD from BD based on gut microbiome signatures.